In the real data world, there are various clustering algorithms available in data mining. The data
available from the different data sources may be huge in instances, attributes and in different formats.
The clustering algorithms available are assessed based on how the algorithm cluster the given data and
find its parametric values. The clustering of data may end in inappropriate results if the algorithm is
not chosen wisely. This paper proposes a comparison between diverse clustering algorithms such as K
Means clustering, Mini-Batch K Means clustering, Hierarchical clustering, Bagging and Boosting by
figuring out clustering strategies using high dimensional datasets on each algorithm above. After the
process of data cleaning in dataset, we have clustered the datasets and compared the summary of each
to showcase the comparability of difference in their strategical values such as Clustering tendency,
clustering quality and data driven approach for evaluating the number of clusters, Normalized Mutual
Information (NMI) metric and provide an idea to choose the algorithm for clustering the data
effectively. And as a result, Local Clustering Coefficient (LCC) with K-means clustering bunching
method performs better than the other clustering algorithms and the results are reported.

plus-circle Add Review

comment

Reviews